Source code for pygan.noisesampler.image_noise_sampler
# -*- coding: utf-8 -*-
import numpy as np
from pygan.noise_sampler import NoiseSampler
from pydbm.cnn.featuregenerator.image_generator import ImageGenerator
[docs]class ImageNoiseSampler(NoiseSampler):
'''
Sampler which draws samples from the noise prior of images.
'''
def __init__(
self,
batch_size,
image_dir,
seq_len=None,
gray_scale_flag=True,
wh_size_tuple=(100, 100),
norm_mode="z_score"
):
'''
Init.
Args:
training_image_dir: Dir path which stores image files for training.
test_image_dir: Dir path which stores image files for test.
seq_len: The length of one sequence.
gray_scale_flag: Gray scale or not(RGB).
wh_size_tuple: Tuple(`width`, `height`).
norm_mode: How to normalize pixel values of images.
- `z_score`: Z-Score normalization.
- `min_max`: Min-max normalization.
- `tanh`: Normalization by tanh function.
'''
self.__feature_generator = ImageGenerator(
epochs=1,
batch_size=batch_size,
training_image_dir=image_dir,
test_image_dir=image_dir,
seq_len=seq_len,
gray_scale_flag=gray_scale_flag,
wh_size_tuple=wh_size_tuple,
norm_mode=norm_mode
)
self.__norm_mode = norm_mode
[docs] def generate(self):
'''
Draws samples from the `true` distribution.
Returns:
`np.ndarray` of samples.
'''
observed_arr = None
for result_tuple in self.__feature_generator.generate():
observed_arr = result_tuple[0]
break
if self.noise_sampler is not None:
self.noise_sampler.output_shape = observed_arr.shape
observed_arr += self.noise_sampler.generate()
observed_arr = observed_arr.astype(float)
if self.__norm_mode == "z_score":
if observed_arr.std() != 0:
observed_arr = (observed_arr - observed_arr.mean()) / observed_arr.std()
elif self.__norm_mode == "min_max":
if (observed_arr.max() - observed_arr.min()) != 0:
observed_arr = (observed_arr - observed_arr.min()) / (observed_arr.max() - observed_arr.min())
elif self.__norm_mode == "tanh":
observed_arr = np.tanh(observed_arr)
return observed_arr